{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Multivariate relationships"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import linregress\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('whitegrid')\n",
        "plt.style.use(\"fivethirtyeight\")\n",
        "%matplotlib inline\n",
        "\n",
        "# For reading stock data from yahoo\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:02.020Z",
          "iopub.execute_input": "2021-05-28T02:15:02.025Z",
          "iopub.status.idle": "2021-05-28T02:15:02.764Z",
          "shell.execute_reply": "2021-05-28T02:15:02.801Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = '2020-01-01'\n",
        "end = '2020-12-31'\n",
        "\n",
        "symbol = 'AMD'"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:02.770Z",
          "iopub.execute_input": "2021-05-28T02:15:02.774Z",
          "iopub.status.idle": "2021-05-28T02:15:02.782Z",
          "shell.execute_reply": "2021-05-28T02:15:02.805Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = yf.download(symbol, start, end)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:02.789Z",
          "iopub.execute_input": "2021-05-28T02:15:02.793Z",
          "iopub.status.idle": "2021-05-28T02:15:03.872Z",
          "shell.execute_reply": "2021-05-28T02:15:03.987Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2020-01-02  46.860001  49.250000  46.630001  49.099998  49.099998  80331100\n2020-01-03  48.029999  49.389999  47.540001  48.599998  48.599998  73127400\n2020-01-06  48.020000  48.860001  47.860001  48.389999  48.389999  47934900\n2020-01-07  49.349998  49.389999  48.040001  48.250000  48.250000  58061400\n2020-01-08  47.849998  48.299999  47.139999  47.830002  47.830002  53767000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>46.860001</td>\n      <td>49.250000</td>\n      <td>46.630001</td>\n      <td>49.099998</td>\n      <td>49.099998</td>\n      <td>80331100</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>48.029999</td>\n      <td>49.389999</td>\n      <td>47.540001</td>\n      <td>48.599998</td>\n      <td>48.599998</td>\n      <td>73127400</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>48.020000</td>\n      <td>48.860001</td>\n      <td>47.860001</td>\n      <td>48.389999</td>\n      <td>48.389999</td>\n      <td>47934900</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>49.349998</td>\n      <td>49.389999</td>\n      <td>48.040001</td>\n      <td>48.250000</td>\n      <td>48.250000</td>\n      <td>58061400</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>47.849998</td>\n      <td>48.299999</td>\n      <td>47.139999</td>\n      <td>47.830002</td>\n      <td>47.830002</td>\n      <td>53767000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:03.879Z",
          "iopub.execute_input": "2021-05-28T02:15:03.884Z",
          "iopub.status.idle": "2021-05-28T02:15:03.896Z",
          "shell.execute_reply": "2021-05-28T02:15:03.991Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2020-12-23  93.080002  93.129997  91.459999  91.550003  91.550003  25993300\n2020-12-24  91.800003  92.510002  91.309998  91.809998  91.809998  16705900\n2020-12-28  92.930000  93.139999  90.820000  91.599998  91.599998  30627300\n2020-12-29  91.660004  92.459999  89.430000  90.620003  90.620003  31748200\n2020-12-30  90.779999  92.849998  90.190002  92.290001  92.290001  25845000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-12-23</th>\n      <td>93.080002</td>\n      <td>93.129997</td>\n      <td>91.459999</td>\n      <td>91.550003</td>\n      <td>91.550003</td>\n      <td>25993300</td>\n    </tr>\n    <tr>\n      <th>2020-12-24</th>\n      <td>91.800003</td>\n      <td>92.510002</td>\n      <td>91.309998</td>\n      <td>91.809998</td>\n      <td>91.809998</td>\n      <td>16705900</td>\n    </tr>\n    <tr>\n      <th>2020-12-28</th>\n      <td>92.930000</td>\n      <td>93.139999</td>\n      <td>90.820000</td>\n      <td>91.599998</td>\n      <td>91.599998</td>\n      <td>30627300</td>\n    </tr>\n    <tr>\n      <th>2020-12-29</th>\n      <td>91.660004</td>\n      <td>92.459999</td>\n      <td>89.430000</td>\n      <td>90.620003</td>\n      <td>90.620003</td>\n      <td>31748200</td>\n    </tr>\n    <tr>\n      <th>2020-12-30</th>\n      <td>90.779999</td>\n      <td>92.849998</td>\n      <td>90.190002</td>\n      <td>92.290001</td>\n      <td>92.290001</td>\n      <td>25845000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:03.903Z",
          "iopub.execute_input": "2021-05-28T02:15:03.907Z",
          "iopub.status.idle": "2021-05-28T02:15:03.917Z",
          "shell.execute_reply": "2021-05-28T02:15:03.994Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()\n",
        "df2 = df.copy()"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:03.924Z",
          "iopub.execute_input": "2021-05-28T02:15:03.930Z",
          "iopub.status.idle": "2021-05-28T02:15:03.937Z",
          "shell.execute_reply": "2021-05-28T02:15:03.997Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run regression with linregress\n",
        "data = df1.dropna(subset=['Close', 'Open'])\n",
        "xs = data['Open']\n",
        "ys = data['Close']\n",
        "res = linregress(xs, ys)\n",
        "print(res)\n",
        "\n",
        "# Run regression with StatsModels\n",
        "results = smf.ols('Close ~ Open', data = df).fit()\n",
        "print(results.params)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinregressResult(slope=0.9913260730102901, intercept=0.5903513716228872, rvalue=0.9941976773743896, pvalue=4.2296336523703754e-244, stderr=0.006783577950096053)\n",
            "Intercept    0.590351\n",
            "Open         0.991326\n",
            "dtype: float64\n"
          ]
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:03.944Z",
          "iopub.execute_input": "2021-05-28T02:15:03.949Z",
          "shell.execute_reply": "2021-05-28T02:15:04.000Z",
          "iopub.status.idle": "2021-05-28T02:15:03.960Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by Volume\n",
        "grouped = df.groupby('Volume')\n",
        "\n",
        "# Compute mean Adj Close in each group\n",
        "mean_adj_close = grouped['Adj Close'].mean()\n",
        "\n",
        "# Plot mean income as a scatter plot\n",
        "plt.plot(mean_adj_close, 'o', alpha = 0.5)\n",
        "\n",
        "# Label the axes\n",
        "plt.xlabel('Volume', color='r')\n",
        "plt.ylabel('Adj Close')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:03.967Z",
          "iopub.execute_input": "2021-05-28T02:15:03.971Z",
          "iopub.status.idle": "2021-05-28T02:15:04.046Z",
          "shell.execute_reply": "2021-05-28T02:15:04.038Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a new column with educ squared\n",
        "df['Close2'] = df['Close'] ** 2\n",
        "\n",
        "# Run a regression model with educ, educ2, age, and age2\n",
        "results = smf.ols('Open ~ Close + Close2 + High + Low', data = df).fit()\n",
        "\n",
        "# Print the estimated parameters\n",
        "print(results.params)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept   -0.382706\n",
            "Close       -0.615110\n",
            "Close2      -0.000062\n",
            "High         0.803877\n",
            "Low          0.823298\n",
            "dtype: float64\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:04.058Z",
          "iopub.execute_input": "2021-05-28T02:15:04.065Z",
          "iopub.status.idle": "2021-05-28T02:15:04.079Z",
          "shell.execute_reply": "2021-05-28T02:15:04.165Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2020-01-02  46.860001  49.250000  46.630001  49.099998  49.099998  80331100\n2020-01-03  48.029999  49.389999  47.540001  48.599998  48.599998  73127400\n2020-01-06  48.020000  48.860001  47.860001  48.389999  48.389999  47934900\n2020-01-07  49.349998  49.389999  48.040001  48.250000  48.250000  58061400\n2020-01-08  47.849998  48.299999  47.139999  47.830002  47.830002  53767000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>46.860001</td>\n      <td>49.250000</td>\n      <td>46.630001</td>\n      <td>49.099998</td>\n      <td>49.099998</td>\n      <td>80331100</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>48.029999</td>\n      <td>49.389999</td>\n      <td>47.540001</td>\n      <td>48.599998</td>\n      <td>48.599998</td>\n      <td>73127400</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>48.020000</td>\n      <td>48.860001</td>\n      <td>47.860001</td>\n      <td>48.389999</td>\n      <td>48.389999</td>\n      <td>47934900</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>49.349998</td>\n      <td>49.389999</td>\n      <td>48.040001</td>\n      <td>48.250000</td>\n      <td>48.250000</td>\n      <td>58061400</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>47.849998</td>\n      <td>48.299999</td>\n      <td>47.139999</td>\n      <td>47.830002</td>\n      <td>47.830002</td>\n      <td>53767000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:04.090Z",
          "iopub.execute_input": "2021-05-28T02:15:04.096Z",
          "iopub.status.idle": "2021-05-28T02:15:04.112Z",
          "shell.execute_reply": "2021-05-28T02:15:04.169Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg = smf.ols('Volume ~ Close + Close2 + High + Low', data = df).fit()\n",
        "\n",
        "# Make the DataFrame\n",
        "ndata = pd.DataFrame()\n",
        "ndata['Close'] = np.linspace(0, 20)\n",
        "ndata['High'] = df2['High'].max()\n",
        "ndata['Close2'] = df2['Close']**2\n",
        "ndata['Low'] = df2['Low']**2\n",
        "\n",
        "# Generate and plot the predictions\n",
        "pred = reg.predict(ndata)\n",
        "print(pred.head())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0   NaN\n",
            "1   NaN\n",
            "2   NaN\n",
            "3   NaN\n",
            "4   NaN\n",
            "dtype: float64\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:04.120Z",
          "iopub.execute_input": "2021-05-28T02:15:04.125Z",
          "iopub.status.idle": "2021-05-28T02:15:04.136Z",
          "shell.execute_reply": "2021-05-28T02:15:04.172Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot mean Volume in each age group\n",
        "plt.clf()\n",
        "grouped = df.groupby('Adj Close')\n",
        "mean_open = grouped['Open'].mean()\n",
        "plt.plot(mean_open, 'o', alpha = 0.5)\n",
        "\n",
        "# Plot the predictions\n",
        "pred = results.predict(df)\n",
        "plt.plot(df['Adj Close'], pred, label='Adj Close')\n",
        "\n",
        "# Label axes\n",
        "plt.xlabel('Adj Close')\n",
        "plt.ylabel('Open')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-05-28T02:15:04.146Z",
          "iopub.execute_input": "2021-05-28T02:15:04.151Z",
          "iopub.status.idle": "2021-05-28T02:15:04.240Z",
          "shell.execute_reply": "2021-05-28T02:15:04.247Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}